DiamiR Biosciences Partners with JADBio to Utilize Machine Learning in Assay Development

DiamiR Biosciences, a developer of innovative non-invasive blood-based diagnostic tests for brain health and other diseases announced that it has entered into an agreement with JADBio to utilize its Automated Machine Learning (AutoML) Platform and Services for development of predictive models based on DiamiR’s microRNA panels and other factors for risk assessment in Alzheimer’s disease and Rett syndrome.

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Alidad Mireskandari, Ph.D., DiamiR’s CEO commented that “applying Machine Learning capabilities to our microRNA platform is a key step towards validation of our diagnostic tests.  We are excited about partnering with JADBio, a leader in Automated Machine Learning in life sciences, to develop sophisticated, proprietary ML based testing algorithms to further our development efforts.”

“We are thrilled to be partnering with DiamiR,” said JADBio’s CEO, Pavlos Charonyktakis, “as we are confident that our state-of-the-art ML platform and services will prove instrumental in DiamiR’s efforts to efficiently validate their biomarker panels and develop highly accurate diagnostic tests.”

DiamiR is a privately held molecular diagnostics company focused on developing minimally invasive tests for detection and monitoring of pathology based on quantitative analysis of organ-enriched microRNA signatures in plasma for screening, patient stratification, as well as disease progression and treatment monitoring. DiamiR collaborates with leading academic centers, disease foundations, and biopharma companies. For more information, please visit the company’s

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JADBio is an AutoML-based Bioinformatics platform, allowing Life Scientists to effortlessly make novel biomarker discoveries and build unique predictive models from publicly available or own-study data, without the need for coding or ML expertise. With JADBio, bioinformaticians and data scientists can analyze any type of biomolecular data, while through the API they can access features such as image analysis, batch analysis, and SciKit Learn algorithm incorporation.

This news release contains forward-looking statements regarding future events. These statements are predictions and are subject to risks and uncertainties that could cause the actual events or results to differ materially.

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